Our main contributions in this work are two-folds: we propose to merge the CMSFF in the backbone network to enhance the feature representation, and combine the CA to focus on the informative channels; we propose a group of training strategies to match the different generalization scenarios. Although FSL is very suitable for plant disease recognition, the applications of smart agriculture have just begun . In this research direction,there are still huge potential space needed to explore. In here, we discuss the limitations of this work and some future works. 1. Multi-disease. The PV and AFD used in this work as target data which have a common characteristic that only single disease is included in per image. In fact, once a plant is infected by the first disease, it is easily infected by other diseases because the immune system is attacked and becomes weak . Multiple diseases occur in a plant is more common in the real field condition. But the combinations of different diseases are too many to collect sufficient samples for each category from classification perspective . The current researches prefer to solve this problem by semantic segmentation. We do not cover this challenging problem due to limitations of data resources in this work. 2. Formulation of meta-learning data. The samples of PV were taken under controlled condition , which have a clean board as the unified background,dutch buckets for sale the illumination is under controlled, only single leaf in per image, only single disease occurs in per leaf. The settings are simple and very different from the in-wild conditions.
That is the reason many researches already achieved high accuracy by using deep learning CNNs on PV . But the samples of AFD were taken under in-wild condition, which have complex surroundings. When testing with AFD, we use PV in meta learning, mainly considering that both datasets are about plant diseases. Since we did not find any other appropriate dataset, the degree of similarity of the data used in training and test was not taken in account. According to our hypothesis, the degree of similarity of data used in meta-learning and test is higher, the adapting is easier, and the result would be better. It is demonstrated that the selection of meta-learning data is critical in this pipeline. The data used in meta-learning stage should be determined by the target. When the application scenarios cannot be predicted, how to formulate an appropriate meta-learning dataset is worthy to study. Inspired by Nuthalapati and Tunga and Li and Yang , the effectiveness of a mixed dataset for meta learning will be considered. 3. Sub-class classification. For the application of plant disease recognition, it is more meaningful to distinguish the diseases belonging to the same species. What farmers need more than anything else is a diagnostic assistant that can identify similar diseases belonging to the same plant. Although sub-class classification is difficult , it is an inescapable work in plant disease recognition and the performance is needed to be improved urgently. Fine-grained features of the lesions being the distinguishable features to solve this issue. In this direction, lesion detection and segmentation, fine-grained visual classification are involved. 4. The quality and quantity of training data. Most of the current researches of FSL deal with the configuration of data used in test, but very little work has concerned the data used in training. The common sense is that deep learning networks rely on large-scale data. However, a new direction is discussing the quality and quantity of training data recently . These works indicate that part of data can achieve at the same performance as full data.
Date quality can be assessed, which can guide to establish a dataset with enough diversity data while without redundant samples. The networks of appropriate depth using good data can achieve optimal results in many traditional CNN classification tasks. In this work, we use large-scale data in base-training and meta-learning. The quantity of data follows the conventional settings for comparison purposes. The data quality assessment work is not involved in this work. For the specific topic of plant disease, the data quality is very important. We know that at different stages of development of plants and diseases, the symptom appearances are very different. How to construct a comprehensive set without redundant data to represent a disease is a valuable work in the future . 5. Cross-domain. The significance of cross-domain has been introduced in prior sections. We emphasize cross-domain again because it is common when we cannot predict the species, surroundings, and photo conditions in test. In this work, we consider it from training strategies. There are many aspects to explore in future work, such as network architecture, feature distribution calibration etc.The Pajaro Valley is a productive agricultural region located on the Central California coastline, spanning across portions of Monterey and Santa Cruz counties. It provides an excellent study region for examining the implications of seawater intrusion on agriculture. Seawater intrusion in the region is well-documented, severe, and increasing over time. The local water management agency has done a rigorous job in tracking changes in salinity and agricultural production, as well as monitoring groundwater use. In addition, the region has engaged in large-scale mitigation strategy efforts, with the use of municipal treated wastewater. While the Pajaro Valley has experienced more severe seawater intrusion issues than most coastal agricultural regions, it is also an early adopter of recycled water as an alternative water source. Understanding the dynamics of seawater intrusion and management in this region can provide important implications for other regions wrestling with salinity problems under climate change.
In the Pajaro Valley, approximately 30% of the land and 85% of total water consumption is used for agricultural production. Due to the temperate Mediterranean micro-climate, Pajaro Valley has some of highest valued land in the country. In 2019, the crop revenue generated by the Pajaro Valley was approximately $1 billion across 28,500 irrigated acres . The region is well known for a variety of produce, including strawberries, raspberries and blackberries , apples, artichokes, and vegetable and nursery products. The major companies Driscoll’s and Martinelli’s are headquartered in the valley. Many of the crops in the Pajaro Valley require a significant amount of water for production, with most requiring between 2-3 acre ft. With virtually no access to surface water , irrigation water is sourced.Groundwater is the primary source of water for the entire basin, making up 93% of the water used in 2020. In fact, the Central Coast relies mostly on groundwater for agriculture,hydroponic net pots although a few farmers receive water through surface sources, the State Water Project and the Central Valley Project . Less than one percent of the Pajaro Valley’s water supply came from surface sources in 2020 . . On average, total annual groundwater use from 2010-2020 typically ranged from about 50,000-55,000 acre-feet per year , although this increased up to 60,000 AFY during the height of the 2013-2015 drought . Groundwater pumping in the Pajaro Valley is nearly twice the sustainable yield of the basin annually, which is defined as the quantity of water that enters the basin, through agricultural runoff and precipitation. By the 1940s, groundwater depletion was significant enough for growers to adopt deep well turbine pumps from the oil industry in order to reach the groundwater . Artesian wells, which were prevalent until this era, started to be artesian only during winter . The installation of the tube wells has led to an additional, significant groundwater concern: that of seawater intrusion. Seawater intrusion is the process of ocean water entering groundwater tables, contaminating freshwater resources. Many factors contribute to saltwater intrusion, including irrigation wells, excess pumping of groundwater, climate change, and sea-level rise. Mechanically, seawater intrusion works across four major dimensions. In the Pajaro Valley, the primary movement of seawater into freshwater is lateral . When a groundwater aquifer falls below sea-level, it creates a landward gradient where the dense seawater moves horizontally into the freshwater. Secondly, major storms and coastal flooding lead to seawater inundating nearby land, resulting in seawater percolating through the soil and leaching into the underlying groundwater. Additionally, seawater can enter coastal groundwater aquifers from below, since groundwater commonly sits directly on top of seawater, with only the relative density difference separating the two water bodies.
The use of tube wells in the freshwater aquifer leads to pressure changes, where the resulting “cones of depression” allow seawater to mix upwards into the freshwater aquifer. Finally, sea-level rise intermingles with seawater intrusion in multiple ways: by increasing the frequency and severity of coastal flooding, and by increasing the extent of the seawater “toe”, or how far inland the seawater sits below the groundwater aquifer. Altogether, seawater intrusion is a complex, dynamic system that is difficult to combat once in motion. Seawater intrusion was first noticed in the Pajaro Valley in 1951, with the extent of seawater intrusion increasing seven-fold since its discovery. However, in years of high rainfall, groundwater levels were historically high enough to prevent significant seawater intrusion. Simulations from the Pajaro Valley Hydrologic Model suggest that before the 1984- 1992 drought, groundwater levels only dropped below sea level during drought years. Since 1984 however, the groundwater level has been on a largely continuous decline . In 2010, the Pajaro Valley Water Management Agency reported that long term rates of saline intrusion are about 200 ft/year, and intrusion renders 11,000 acre-feet of water unusable annually. One-half of the groundwater table is below sea-level year round, and two-thirds is below sea level after irrigation season in the fall. Even with fluctuations in rainfall, in much of the Pajaro Valley, today the groundwater table remains consistently below sea level . Seawater intrusion is typically measured by the concentration of chloride present in a water body. However, for agricultural purposes, chlorides affect crops and yields in the same ways as other salts that may be present in irrigation water. Total salt content is generally measured using electrical conductivity and Total Dissolved Solids . Irrigation water with a salinity value of less than 500 mg/L TDS is the objective for irrigated agriculture. Strawberries, however, are a particularly salt-sensitive crop, with yields beginning to decline at TDS values of 450 mg/L . Irrigation water that has high TDS levels can lead to root and foliar absorption, negatively impacting crop yields1 . The relationship between irrigation water salinity and crop yields is depicted in Figure 3.4. Plants can typically tolerate salinity in irrigation water up until a crop-specific threshold, at which point yields decline linearly. Additionally, irrigation water that is high in sodium can lead to a loss of soil permeability, especially for soils with a lot of clay . While salinity issues especially impact the coast of the Pajaro Valley, TDS levels vary significantly across the region. As discussed above, there are many channels for seawater intrusion, and transport of water between aquifer layers is possible. Groundwater will move from areas of high to low pressure, through naturally occurring gaps, vertically, or through well bores. The Murphy Crossing area, on the eastern side of the Pajaro Valley, contains especially high levels of total dissolved solids . The highest chloride levels tend to occur in aquifers consisting of Aromas Red Sands and the Purisima geologic formation,with values from fewer than 5 mg/L to 14,600 mg/L. The average total dissolved solids levels across the Pajaro Valley, from 2003-2020, are shown in Figure? Salinity also varies significantly over time, due to changes in precipitation and groundwater use. There are other pollutants that lead to water quality concerns in the Pajaro Valley, including nitrates and phosphates. However, while nitrates and phosphates are of concern to human and environmental health, they do not have a negative impact on crop yields. The Central Coast Regional Water Quality Control Board has water quality objectives for its irrigation supplies . Nitrate contamination is largely due to fertilizer, while the source of salts is primarily saltwater intrusion, although seawater also contains nitrates. Therefore, while these contaminants are essential to keep track of, for agricultural producers, the concerns are negligible.While seawater intrusion had been discovered in the Pajaro Valley in the 1950s, broader management did not take place until a California-wide drought in the late 1970s spurred statewide action. In 1977, the Governor’s Commission to Review Water Rights in California was created, and their report contained recommendations to improve groundwater management and overdraft.