Tropical peanut maturation scale for harvesting seeds with superior quality.

Determining the moment for harvesting the tropical peanut with a focus on superior seed quality is not an easy task. Particularities such as indeterminate flowering, underground fruiting and uneven maturation further increase this technical challenge. It is in this context that we aim to investigate harvest indicators based on the maturation and late maturation phases of tropical peanuts to obtain seeds with superior physiological and health quality. The plants were grown in field conditions and their development stages were carefully monitored until seed production. The water content, dry weight, germination capacity, desiccation tolerance, vigor, longevity, and seed pathogens were evaluated throughout these stages. We showed that seeds from early stages (R5 and R6) did not fully tolerate desiccation and were highly sensitive to pathogen contamination after storage (Aspergillus, Penicillium, and Bacteria). At late stages (R7, R8, and R9), the seeds had optimized vigor, longevity and bioprotection against fungi and thermal stress. The peanut maturation scale for tropical agriculture provides unique harvesting guidelines that make it possible to monitor the plants' development stages with a focus on producing superior quality seeds.

Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality.

The sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi (A. flavus, A. niger, Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of peanut seed health quality were found. The incubation period of 216 h was the one that most contributed to discriminating healthy seeds from those containing fungi through multispectral images. Texture (Percent Run), color (CIELab L*) and reflectance (490 nm) were highly effective in discriminating the sanitary quality of peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status (90 to 100%). Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.

Quality of cowpea seeds: A food security strategy in the tropical environment.

What is the relation between seed quality and food security? Here we built a summary diagram that links the development stages of the seeds with their potential of providing grain yield. This idea was tested using cowpea as a model crop, grown in a tropical environment. Initially, seed quality attributes such as water content, dry weight, germination, vigor, and longevity were characterized during seed development. With this, we were able to elucidate at which point the late maturation phase and the acquisition of seed with superior physiological quality starts. From these data, the proposed summary diagram highlighted the seed quality as a technological basis for generating a more productive plant community. It also showed that only seeds with a high-quality profile have a better chance to establishment in an increasingly challenging agricultural environment. Overall, we bring the concept that cowpea seeds with superior quality besides being the essential input for tropical agriculture is also a strategy that can contribute food security.

A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms.

In recent years, technological innovations have allowed significant advances in the diagnosis of seed quality. Seeds with superior physiological quality are those with the highest level of physiological maturity and the integration of rapid and precise methods to separate them contributes to better performance in the field. Autofluorescence-spectral imaging is an innovative technique based on fluorescence signals from fluorophores present in seed tissues, which have biological implications for seed quality. Thus, through this technique, it would be possible to classify seeds in different maturation stages. To test this, we produced plants of a commercial cultivar (MG/BR 46 "Conquista") and collected the seeds at five reproductive (R) stages: R7.1 (beginning of maturity), R7.2 (mass maturity), R7.3 (seed disconnected from the mother plant), R8 (harvest point), and R9 (final maturity). Autofluorescence signals were extracted from images captured at different excitation/emission combinations. In parallel, we investigated physical parameters, germination, vigor and the dynamics of pigments in seeds from different maturation stages. To verify the accuracy in predicting the seed maturation stages based on autofluorescence-spectral imaging, we created machine learning models based on three algorithms: (i) random forest, (ii) neural network, and (iii) support vector machine. Here, we reported the unprecedented use of the autofluorescence-spectral technique to classify the maturation stages of soybean seeds, especially using the excitation/emission combination of chlorophyll a (660/700 nm) and b (405/600 nm). Taken together, the machine learning algorithms showed high performance segmenting the different stages of seed maturation. In summary, our results demonstrated that the maturation stages of soybean seeds have their autofluorescence-spectral identity in the wavelengths of chlorophylls, which allows the use of this technique as a marker of seed maturity and superior physiological quality.

An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality.

Seeds of high physiological quality are defined by their superior germination capacity and uniform seedling establishment. Here, it was investigated whether multispectral images combined with machine learning models can efficiently categorize the quality of peanut seedlots. The seed quality from seven lots was assessed traditionally (seed weight, water content, germination, and vigor) and by multispectral images (area, length, width, brightness, chlorophyll fluorescence, anthocyanin, and reflectance: 365 to 970 nm). Seedlings from the seeds of each lot were evaluated for their photosynthetic capacity (fluorescence and chlorophyll index, F0, Fm, and Fv/Fm) and stress indices (anthocyanin and NDVI). Artificial intelligence features (QDA method) applied to the data extracted from the seed images categorized lots with high and low quality. Higher levels of anthocyanin were found in the leaves of seedlings from low quality seeds. Therefore, this information is promising since the initial behavior of the seedlings reflected the quality of the seeds. The existence of new markers that effectively screen peanut seed quality was confirmed. The combination of physical properties (area, length, width, and coat brightness), pigments (chlorophyll fluorescence and anthocyanin), and light reflectance (660, 690, and 780 nm), is highly efficient to identify peanut seedlots with superior quality (98% accuracy).